Deep Learning for IoT Big Data and Streaming Analytics: A Survey
- M. Mohammadi, A. Al-Fuqaha, Sameh Sorour, M. Guizani
- Computer ScienceIEEE Communications Surveys and Tutorials
- 9 December 2017
A thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain and discusses why DL is a promising approach to achieve the desired analytics in these types of data and applications.
Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges
- Hazim Shakhatreh, A. Sawalmeh, M. Guizani
- Computer ScienceIEEE Access
- 19 April 2018
The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue,…
Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services
- M. Mohammadi, A. Al-Fuqaha, M. Guizani, Jun-Seok Oh
- Computer ScienceIEEE Internet of Things Journal
- 1 April 2018
This paper proposes a semisupervised DRL model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of the learning agent and utilizes variational autoencoders as the inference engine for generalizing optimal policies.
Blockchain for AI: Review and Open Research Challenges
- K. Salah, M. H. Rehman, Nishara Nizamuddin, A. Al-Fuqaha
- Computer ScienceIEEE Access
- 2019
This paper reviews the literature, tabulate, and summarize the emerging blockchain applications, platforms, and protocols specifically targeting AI area, and identifies and discusses open research challenges of utilizing blockchain technologies for AI.
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
- M. Usama, Junaid Qadir, A. Al-Fuqaha
- Computer ScienceIEEE Access
- 19 September 2017
A comprehensive survey highlighting recent advancements in unsupervised learning techniques, and describe their applications in various learning tasks, in the context of networking is provided.
Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning
- Inaam Ilahi, M. Usama, D. Niyato
- Computer ScienceIEEE Transactions on Artificial Intelligence
- 27 January 2020
This work investigates the vulnerabilities that an adversary can exploit to attack DRL along with state-of-the-art countermeasures to prevent such attacks and highlights open issues and research challenges for developing solutions to deal with attacks on DRL-based intelligent systems.
Systematization of Knowledge (SoK): A Systematic Review of Software-Based Web Phishing Detection
- Zuochao Dou, Issa M. Khalil, Abdallah Khreishah, A. Al-Fuqaha, M. Guizani
- Computer ScienceIEEE Communications Surveys and Tutorials
- 13 September 2017
This paper presents a systematic study of phishing detection schemes, especially software based ones, and studies evaluation datasets, detection features, detection techniques, and evaluation metrics to provide insights that will help guide the development of more effective and efficient phishing Detection schemes.
Exploiting Unlabeled Data in Smart Cities using Federated Learning
- A. Albaseer, Bekir Sait Ciftler, M. Abdallah, A. Al-Fuqaha
- Computer ScienceArXiv
- 10 January 2020
This work proposes a semi-supervised federated learning method called FedSem that exploits unlabeled data and shows that FedSem can improve accuracy up to 8% by utilizing the unlabeling data in the learning process.
Secure and Robust Machine Learning for Healthcare: A Survey
- A. Qayyum, Junaid Qadir, M. Bilal, A. Al-Fuqaha
- Computer Science, MedicineIEEE Reviews in Biomedical Engineering
- 21 January 2020
An overview of various application areas in healthcare that leverage ML techniques from security and privacy point of view and present associated challenges and potential methods to ensure secure and privacy-preserving ML for healthcare applications is presented.
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward
- A. Qayyum, M. Usama, Junaid Qadir, A. Al-Fuqaha
- Computer ScienceIEEE Communications Surveys and Tutorials
- 29 May 2019
An in-depth overview of the various challenges associated with the application of ML in vehicular networks is presented and a solution to defend against adversarial attacks in multiple settings is outlined.
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